Remote Sensing to monitor Water Use Efficency in Agriculture

About me

  • Darius Görgen
  • B.A. Political Sciences & B.Sc. Geography @University of Marburg
  • M.Sc. thesis: Predicting violent conflict induced by environmental changes
  • Passionate about OpenSource, OpenData, OpenScience with R and Python
  • slides @https://github.com/goergen95/clca

What am I going to talk about?

  • ICARDA’s project on conservation agriculture

  • some basics of remote sensing

  • exploring available data sets

  • outline the work to be done

Research project

Conservation Agriculture in crop-livestock systems (CLCA) in the drylands

  • ongoing ICARDA project (2018 - 2022) financed by an IFAD grant of 2.5 Mio. USD

  • implementation of conservation agriculture practices to increase water use efficiency, soil protection and income

  • ICARDA expressed interest in using RS technologies for ongoing M&E of key indicators of the project

  • in the future: modeling of impacts on water balance and biomass productivity on water basin scale

Conservation agriculture (CA)

Conservation Agriculture according to FAO adapted from FAO (2017)

  • 180 Mio. ha (12.5%) of global cropland under CA practices in 2015 (Kassam et. al (2018))
  • criticism was expressed about the impact of CA on smallholder farms (Giller et. al (2015))
  • increase in yields is not observed globally
  • in dry conditions yield increase is more pronounced
  • depending on the implementation

Basics of Remote Sensing

Basic principles of RS

Source: Centre for Remote Imaging, Sensing & Processing

EM-Spectrum

Source: NASA

Solar radiation and atmospheric windows

Source: Centre for Remote Imaging, Sensing & Processing

Spectral response functions of Sentinel-2 A & B

Source: ESA

Spectral signatures of different crops

Source: USGS

Basic geographic data representations

Source: Saab (2003)

Sentinel-2 flight path animation by ESA

Source: ESA

Levels of resolution for RS data



  • spectral resolution
  • spatial resolution
  • temporal resolution

WaPOR Datasets

Screenshot of the WaPOR portal

Source: FAO WaPOR

Relational diagram of data layers in WaPOR

Source: FAO (2020)

Scheme of ETLook model used by WaPOR

Source: FAO (2020)

Area of Interest

Map of Bouhedma National Park and 50km buffer zone

Precipitation Analysis

Yearly and monthly sum of precipitation

Precipitation anomalies

Standardised Precipitation-Evapotranspiration Index (SPEI)

Water Productivity Analysis

Productivity measures for the first and second season

The way forward



  • field boundaries collected by ICARDA coupled with implemented practices
  • collection of a control group (e.g. through PSM)
  • comparisons of TBP, GBWP, and NBWP by groups
  • test for significant differences between treatment and control groups

Sources

Thank you for your attention!



https://github.com/goergen95/clca